Select Threshold Report in the Options column of the evaluation to open the threshold report. This is a graph displaying how precision, recall, and F1 measure change as the match threshold increases or decreases.
Earlier in the tutorial, we decreased the match threshold to 0.62 in order to force a match between the names "Kristy Hart" and "Heart Kirsten". Mouse over the graph at a match threshold of 0.62 to see the performance at that threshold. You will see that while we have perfect recall (no false negatives) at this threshold, precision has significantly decreased (more false positives), resulting in an overall lower F1 measure.
For situations in which false negatives (missed matches) are troublesome or even dangerous, it is best to pick a threshold that favors recall. But imagine we are using RMS to match medical records; in this case, it is important we don't have so many extra matches (false positives) that it is impossible to find the right one (true positive). Such a situation could result in someone receiving incorrect medical care. With that in mind, let's take another approach to tune our model for this scenario.